Class 8 Lab 7 Assignment Steps:

Part I: Data cleaning and organization:

2018 Dataset

Note: #2 map in examples above uses income as the social vulnerability in place of mobile home structures. This produces a different thematic map, but similar spatial pattern.

  • Step 2, Resolve Map Projections:

  • Check the input data .prj; notice that mobile_home_percentage_county.shp is not in the same projection system as is 1950-2018-torn-aspath.shp. This assignment requires accurate length calculations; both layers in an appropriate projection system is required.

    • The mobile home county polygons are currently projected as USA Contiguous Lambert Conformal Conic; this will be used for both layers.

Map Projections for Input Data

  • To Start, open QGIS and create/save a project. Load mobile_home_percentage_county.zip. Note that QGIS does not recognize the projection system. To correct, search for USA Contiguous Lambert Conformal Conic in the Project Properties CRS; note that EPSG:102004 is filed under the Authority ID. Select this projection for the Project CRS.

Projection Selection

  • Next, load 1950-2018-torn-aspath.zip. Reproject and export the layer to the project directory:

Reprojection

  • Step 3, fixed geometries on both layers:

  • Like assignment 7, both layers need typology fixes in order for sucessful overlay analysis. Run Fix geometries on both layers. Export these layers to the project directory as tornado_paths and mobile_home_counties. These two layers will be the Hazard and Vulnerability inputs to the analysis.

Fix Geometries

Part II: Overlay Analysis:

  • Currently many tornado paths in the historical dataset tornado_paths cross multiple counties. In order to derive a count of tornadoes per county, there are several overlay techniques that could be utilized. Here we will use Intersection; the result will be a polyline dataset that contains the attributes of the counties in the mobile_home_counties.

Layer Overlay

  • Run Intersection as follows and output as tornado_county.shp:

Note: this analysis step may take a minute or two to complete.

  • In review of the results, Abbeville, SC is the first county in the attribute table. Note that the tornado segments are now cut at the county boundary as a result of the Intersection:

Intersection Result

Part III: Table Development:

  • With the Intersection complete, a summary table will be developed and joined to mobile_home_counties. The field FIPS will be utilized within the Group Stat plugin, gaining a summary count per FIPS county code. This table will then be joined to mobile_home_counties.

  • First, create a new column one and simply provide a value of 1. This will take a moment to run. When Group Stat calculates a county per FIPS, it will use the 1 value and add up to a total of tornadoes within each FIPS county:

Field Calculator Assignment

  • Next, populate the Group Stat tool as follows, and note the resulting table with tornado counts per FIPS:

Group Stat Population

  • Export the table result as fips_join.csv:

Table Export

  • Load fips_join.csv into the project via Delimited Text as follows:

Load Table

  • Next, utilize *Join attributes by field value:

Table Join

  • Load the tool as follows:

Table Join

Note: 222 counties will result as a NULL join - this is expected. These are counties that do not have a tornado record as seen in the image below:

NULL features

Part IV: Normalize data:

  • As counties vary significantly in size the absolute count of tornadoes per county is not yet comparable. To do so, a constant of 100 sq. miles will be utilized in this step:

  • First, create geometry attributes (area in square meters) in the Joined layer. This will produce a new layer Added geom info:

Geometry Attributes

  • Next, calculate a square miles column with the Field Calculator using the following equation in a new field sq.miles, type decimal. Divide by 100 to normalize size for a final count of tornadoes per 100 sq miles within each county:

(area * 0.00000038610)/100

Areal Normalization

  • Finally, normalize the tornado count per 100 county miles within a new field count.100, type whole number:

"None" / "sq.miles"

Areal Normalization

Part V, Select and Risk Rank for 4 conditions:

Risk Rank

Use the median societal exposure and tornado incidence scores as the break between high and low exposure and incidence - source

  • 4 Rank Conditions:
    • 11.10% = Median Mobile Home per County
    • 3 = Median Tornado segments per 100 miles, per county (when producing this statistic, NULL values are assumed to be value 0.
  • Condition #1 -
    • Low Incidence, Low Exposure
      • LI = <3
      • LE = <11
  • Condition #2 -
    • Low Incidence, High Exposure
      • LI = <3
      • HE = >=11
  • Condition #3 -
    • High Incidence, Low Exposure
      • HI = >=3
      • LE = <11
  • Condition #4 -
    • High Incidence, High Exposure
      • HI = >=3
      • HE = >=11

Note: utilize fields count.100 and MobileHome for Incidence and Exposure, respectively.

  • To Start, make each selection condition #1 through #4 within count.100 and MobileHome. Create a new column risk.rank, type whole number and make assignment 1, 2 , 3 and 4 in this new column based on each selection, respectively:

Note: populate with value -999 to start. Any records with -999 resulting are simply the NULL values - counties that have no tornadoes. Those can either be symbolized in the final map as ‘low’ or as ‘No Incidence Records’:

-999 value assigned to table as default value prior to rank assignments 1 through 4

  • Selection and Assignments for Conditions:

  • Make sure to clear selections after each condition is assigned in risk.rank and Toogle Off and save each assignment before proceeding to next assignment.

  • Condition #1 -

    • Select by Expression: "count.100" <3 and "MobileHome" <11
    • Field Calculator within risk.rank:

Condition 1

  • Condition #2 -

    • Select by Expression: "count.100" <3 and "MobileHome" >=11
    • Field Calculator within risk.rank:

Condition 2

  • Condition #3 -

    • Select by Expression: "count.100" >=3 and "MobileHome" <11
    • Field Calculator within risk.rank:

Condition 3

  • Condition #4 -

    • Select by Expression: "count.100" >=3 and "MobileHome" >=11
    • Field Calculator within risk.rank:

Condition 4

Part VI, Thematic Design:

  • With all conditions met and assignments made within the risk.rank field, thematically map and symbolize as follows:

Thematic Categorial Map Labels

  • As seen in the resulting map, condition 4 exhibits a spatial pattern of concentration centered on the southeastern US counties due to the frequency of tornadoes (Hazard) intersecting human geography by building type (Vulnerability):

Thematic Categorial Map Label + Result